Intelligent Cloud-Based Document Retrieval and Generation using RAG

Abstract

In today’s era of information overload, retrieving relevant documents from vast unstructured datasets is increasingly challenging. Conventional search systems often rely on keyword matching or basic NLP techniques, which fail to capture the semantic meaning of queries, leading to less accurate results. This project introduces a cloud-based Document Retrieval System powered by Retrieval-Augmented Generation , integrating a document retriever with a transformer-based language model for intelligent, context-aware retrieval. By leveraging tokenization, embeddings, and semantic understanding, the system identifies and retrieves the most relevant documents from the database or cloud storage. Users can securely register, upload documents, and perform RAG-powered searches, while also generating contextually accurate summaries or content automatically. To enhance accessibility and performance, a lightweight RAG model is employed, ensuring responsiveness even on moderate hardware. The system combines scalability, efficiency, and usability, supporting applications in academia, business, and enterprise environments. By bridging traditional search with advanced AI generation, this solution delivers high-accuracy retrieval and automated knowledge creation, empowering users to manage and access information intelligently and effectively across large datasets. Keywords— Retrieval-Augmented Generation, Document Retrieval System, Transformer Models, Semantic Search, Natural Language Processing, Cloud Storage, Text Generation, Tokenization, Lemmatization, Embeddings, Intelligent Search, Scalable Architecture, Contextual Retrieval, Information Retrieval, Knowledge Generation.